{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UCI Opportunity dataset (Human Activity Recognition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from typing import List\n",
    "from pathlib import Path\n",
    "from config import data_raw_folder, data_processed_folder\n",
    "from timeeval import Datasets\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for source datasets in /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset and\n",
      "saving processed datasets in /home/projects/akita/data/benchmark-data/data-processed\n"
     ]
    }
   ],
   "source": [
    "dataset_collection_name = \"OPPORTUNITY\"\n",
    "source_folder = Path(data_raw_folder) / \"UCI ML Repository/OPPORTUNITY/dataset\"\n",
    "target_folder = Path(data_processed_folder)\n",
    "\n",
    "print(f\"Looking for source datasets in {source_folder.absolute()} and\\nsaving processed datasets in {target_folder.absolute()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\n",
    "    \"timestamp\", \"Accelerometer RKN^ accX\", \"Accelerometer RKN^ accY\", \"Accelerometer RKN^ accZ\", \"Accelerometer HIP accX\", \"Accelerometer HIP accY\", \"Accelerometer HIP accZ\", \"Accelerometer LUA^ accX\", \"Accelerometer LUA^ accY\", \"Accelerometer LUA^ accZ\", \"Accelerometer RUA_ accX\", \"Accelerometer RUA_ accY\", \"Accelerometer RUA_ accZ\", \"Accelerometer LH accX\", \"Accelerometer LH accY\", \"Accelerometer LH accZ\", \"Accelerometer BACK accX\", \"Accelerometer BACK accY\", \"Accelerometer BACK accZ\", \"Accelerometer RKN_ accX\", \"Accelerometer RKN_ accY\", \"Accelerometer RKN_ accZ\", \"Accelerometer RWR accX\", \"Accelerometer RWR accY\", \"Accelerometer RWR accZ\", \"Accelerometer RUA^ accX\", \"Accelerometer RUA^ accY\", \"Accelerometer RUA^ accZ\", \"Accelerometer LUA_ accX\", \"Accelerometer LUA_ accY\", \"Accelerometer LUA_ accZ\", \"Accelerometer LWR accX\", \"Accelerometer LWR accY\", \"Accelerometer LWR accZ\", \"Accelerometer RH accX\", \"Accelerometer RH accY\", \"Accelerometer RH accZ\", \"InertialMeasurementUnit BACK accX\", \"InertialMeasurementUnit BACK accY\", \"InertialMeasurementUnit BACK accZ\", \"InertialMeasurementUnit BACK gyroX\", \"InertialMeasurementUnit BACK gyroY\", \"InertialMeasurementUnit BACK gyroZ\", \"InertialMeasurementUnit BACK magneticX\", \"InertialMeasurementUnit BACK magneticY\", \"InertialMeasurementUnit BACK magneticZ\", \"InertialMeasurementUnit BACK Quaternion1\", \"InertialMeasurementUnit BACK Quaternion2\", \"InertialMeasurementUnit BACK Quaternion3\", \"InertialMeasurementUnit BACK Quaternion4\", \"InertialMeasurementUnit RUA accX\", \"InertialMeasurementUnit RUA accY\", \"InertialMeasurementUnit RUA accZ\", \"InertialMeasurementUnit RUA gyroX\", \"InertialMeasurementUnit RUA gyroY\", \"InertialMeasurementUnit RUA gyroZ\", \"InertialMeasurementUnit RUA magneticX\", \"InertialMeasurementUnit RUA magneticY\", \"InertialMeasurementUnit RUA magneticZ\", \"InertialMeasurementUnit RUA Quaternion1\", \"InertialMeasurementUnit RUA Quaternion2\", \"InertialMeasurementUnit RUA Quaternion3\", \"InertialMeasurementUnit RUA Quaternion4\", \"InertialMeasurementUnit RLA accX\", \"InertialMeasurementUnit RLA accY\", \"InertialMeasurementUnit RLA accZ\", \"InertialMeasurementUnit RLA gyroX\", \"InertialMeasurementUnit RLA gyroY\", \"InertialMeasurementUnit RLA gyroZ\", \"InertialMeasurementUnit RLA magneticX\", \"InertialMeasurementUnit RLA magneticY\", \"InertialMeasurementUnit RLA magneticZ\", \"InertialMeasurementUnit RLA Quaternion1\", \"InertialMeasurementUnit RLA Quaternion2\", \"InertialMeasurementUnit RLA Quaternion3\", \"InertialMeasurementUnit RLA Quaternion4\", \"InertialMeasurementUnit LUA accX\", \"InertialMeasurementUnit LUA accY\", \"InertialMeasurementUnit LUA accZ\", \"InertialMeasurementUnit LUA gyroX\", \"InertialMeasurementUnit LUA gyroY\", \"InertialMeasurementUnit LUA gyroZ\", \"InertialMeasurementUnit LUA magneticX\", \"InertialMeasurementUnit LUA magneticY\", \"InertialMeasurementUnit LUA magneticZ\", \"InertialMeasurementUnit LUA Quaternion1\", \"InertialMeasurementUnit LUA Quaternion2\", \"InertialMeasurementUnit LUA Quaternion3\", \"InertialMeasurementUnit LUA Quaternion4\", \"InertialMeasurementUnit LLA accX\", \"InertialMeasurementUnit LLA accY\", \"InertialMeasurementUnit LLA accZ\", \"InertialMeasurementUnit LLA gyroX\", \"InertialMeasurementUnit LLA gyroY\", \"InertialMeasurementUnit LLA gyroZ\", \"InertialMeasurementUnit LLA magneticX\", \"InertialMeasurementUnit LLA magneticY\", \"InertialMeasurementUnit LLA magneticZ\", \"InertialMeasurementUnit LLA Quaternion1\", \"InertialMeasurementUnit LLA Quaternion2\", \"InertialMeasurementUnit LLA Quaternion3\", \"InertialMeasurementUnit LLA Quaternion4\", \"InertialMeasurementUnit L-SHOE EuX\", \"InertialMeasurementUnit L-SHOE EuY\", \"InertialMeasurementUnit L-SHOE EuZ\", \"InertialMeasurementUnit L-SHOE Nav_Ax\", \"InertialMeasurementUnit L-SHOE Nav_Ay\", \"InertialMeasurementUnit L-SHOE Nav_Az\", \"InertialMeasurementUnit L-SHOE Body_Ax\", \"InertialMeasurementUnit L-SHOE Body_Ay\", \"InertialMeasurementUnit L-SHOE Body_Az\", \"InertialMeasurementUnit L-SHOE AngVelBodyFrameX\", \"InertialMeasurementUnit L-SHOE AngVelBodyFrameY\", \"InertialMeasurementUnit L-SHOE AngVelBodyFrameZ\", \"InertialMeasurementUnit L-SHOE AngVelNavFrameX\", \"InertialMeasurementUnit L-SHOE AngVelNavFrameY\", \"InertialMeasurementUnit L-SHOE AngVelNavFrameZ\", \"InertialMeasurementUnit L-SHOE Compass\", \"InertialMeasurementUnit R-SHOE EuX\", \"InertialMeasurementUnit R-SHOE EuY\", \"InertialMeasurementUnit R-SHOE EuZ\", \"InertialMeasurementUnit R-SHOE Nav_Ax\", \"InertialMeasurementUnit R-SHOE Nav_Ay\", \"InertialMeasurementUnit R-SHOE Nav_Az\", \"InertialMeasurementUnit R-SHOE Body_Ax\", \"InertialMeasurementUnit R-SHOE Body_Ay\", \"InertialMeasurementUnit R-SHOE Body_Az\", \"InertialMeasurementUnit R-SHOE AngVelBodyFrameX\", \"InertialMeasurementUnit R-SHOE AngVelBodyFrameY\", \"InertialMeasurementUnit R-SHOE AngVelBodyFrameZ\", \"InertialMeasurementUnit R-SHOE AngVelNavFrameX\", \"InertialMeasurementUnit R-SHOE AngVelNavFrameY\", \"InertialMeasurementUnit R-SHOE AngVelNavFrameZ\", \"InertialMeasurementUnit R-SHOE Compass\", \"Accelerometer CUP accX\", \"Accelerometer CUP accY\", \"Accelerometer CUP accZ\", \"Accelerometer CUP gyroX\", \"Accelerometer CUP gyroY\", \"Accelerometer SALAMI accX\", \"Accelerometer SALAMI accY\", \"Accelerometer SALAMI accZ\", \"Accelerometer SALAMI gyroX\", \"Accelerometer SALAMI gyroY\", \"Accelerometer WATER accX\", \"Accelerometer WATER accY\", \"Accelerometer WATER accZ\", \"Accelerometer WATER gyroX\", \"Accelerometer WATER gyroY\", \"Accelerometer CHEESE accX\", \"Accelerometer CHEESE accY\", \"Accelerometer CHEESE accZ\", \"Accelerometer CHEESE gyroX\", \"Accelerometer CHEESE gyroY\", \"Accelerometer BREAD accX\", \"Accelerometer BREAD accY\", \"Accelerometer BREAD accZ\", \"Accelerometer BREAD gyroX\", \"Accelerometer BREAD gyroY\", \"Accelerometer KNIFE1 accX\", \"Accelerometer KNIFE1 accY\", \"Accelerometer KNIFE1 accZ\", \"Accelerometer KNIFE1 gyroX\", \"Accelerometer KNIFE1 gyroY\", \"Accelerometer MILK accX\", \"Accelerometer MILK accY\", \"Accelerometer MILK accZ\", \"Accelerometer MILK gyroX\", \"Accelerometer MILK gyroY\", \"Accelerometer SPOON accX\", \"Accelerometer SPOON accY\", \"Accelerometer SPOON accZ\", \"Accelerometer SPOON gyroX\", \"Accelerometer SPOON gyroY\", \"Accelerometer SUGAR accX\", \"Accelerometer SUGAR accY\", \"Accelerometer SUGAR accZ\", \"Accelerometer SUGAR gyroX\", \"Accelerometer SUGAR gyroY\", \"Accelerometer KNIFE2 accX\", \"Accelerometer KNIFE2 accY\", \"Accelerometer KNIFE2 accZ\", \"Accelerometer KNIFE2 gyroX\", \"Accelerometer KNIFE2 gyroY\", \"Accelerometer PLATE accX\", \"Accelerometer PLATE accY\", \"Accelerometer PLATE accZ\", \"Accelerometer PLATE gyroX\", \"Accelerometer PLATE gyroY\", \"Accelerometer GLASS accX\", \"Accelerometer GLASS accY\", \"Accelerometer GLASS accZ\", \"Accelerometer GLASS gyroX\", \"Accelerometer GLASS gyroY\", \"REED SWITCH DISHWASHER S1\", \"REED SWITCH FRIDGE S3\", \"REED SWITCH FRIDGE S2\", \"REED SWITCH FRIDGE S1\", \"REED SWITCH MIDDLEDRAWER S1\", \"REED SWITCH MIDDLEDRAWER S2\", \"REED SWITCH MIDDLEDRAWER S3\", \"REED SWITCH LOWERDRAWER S3\", \"REED SWITCH LOWERDRAWER S2\", \"REED SWITCH UPPERDRAWER\", \"REED SWITCH DISHWASHER S3\", \"REED SWITCH LOWERDRAWER S1\", \"REED SWITCH DISHWASHER S2\", \"Accelerometer DOOR1 accX\", \"Accelerometer DOOR1 accY\", \"Accelerometer DOOR1 accZ\", \"Accelerometer LAZYCHAIR accX\", \"Accelerometer LAZYCHAIR accY\", \"Accelerometer LAZYCHAIR accZ\", \"Accelerometer DOOR2 accX\", \"Accelerometer DOOR2 accY\", \"Accelerometer DOOR2 accZ\", \"Accelerometer DISHWASHER accX\", \"Accelerometer DISHWASHER accY\", \"Accelerometer DISHWASHER accZ\", \"Accelerometer UPPERDRAWER accX\", \"Accelerometer UPPERDRAWER accY\", \"Accelerometer UPPERDRAWER accZ\", \"Accelerometer LOWERDRAWER accX\", \"Accelerometer LOWERDRAWER accY\", \"Accelerometer LOWERDRAWER accZ\", \"Accelerometer MIDDLEDRAWER accX\", \"Accelerometer MIDDLEDRAWER accY\", \"Accelerometer MIDDLEDRAWER accZ\", \"Accelerometer FRIDGE accX\", \"Accelerometer FRIDGE accY\", \"Accelerometer FRIDGE accZ\", \"LOCATION TAG1 X\", \"LOCATION TAG1 Y\", \"LOCATION TAG1 Z\", \"LOCATION TAG2 X\", \"LOCATION TAG2 Y\", \"LOCATION TAG2 Z\", \"LOCATION TAG3 X\", \"LOCATION TAG3 Y\", \"LOCATION TAG3 Z\", \"LOCATION TAG4 X\", \"LOCATION TAG4 Y\", \"LOCATION TAG4 Z\", \"Locomotion\", \"HL_Activity\", \"LL_Left_Arm\", \"LL_Left_Arm_Object\", \"LL_Right_Arm\", \"LL_Right_Arm_Object\", \"ML_Both_Arms\"\n",
    "]\n",
    "ignore_columns = [\n",
    "    \"Accelerometer CUP accX\", \"Accelerometer CUP accY\", \"Accelerometer CUP accZ\", \"Accelerometer CUP gyroX\", \"Accelerometer CUP gyroY\", \"Accelerometer SALAMI accX\", \"Accelerometer SALAMI accY\", \"Accelerometer SALAMI accZ\", \"Accelerometer SALAMI gyroX\", \"Accelerometer SALAMI gyroY\", \"Accelerometer WATER accX\", \"Accelerometer WATER accY\", \"Accelerometer WATER accZ\", \"Accelerometer WATER gyroX\", \"Accelerometer WATER gyroY\", \"Accelerometer CHEESE accX\", \"Accelerometer CHEESE accY\", \"Accelerometer CHEESE accZ\", \"Accelerometer CHEESE gyroX\", \"Accelerometer CHEESE gyroY\", \"Accelerometer BREAD accX\", \"Accelerometer BREAD accY\", \"Accelerometer BREAD accZ\", \"Accelerometer BREAD gyroX\", \"Accelerometer BREAD gyroY\", \"Accelerometer KNIFE1 accX\", \"Accelerometer KNIFE1 accY\", \"Accelerometer KNIFE1 accZ\", \"Accelerometer KNIFE1 gyroX\", \"Accelerometer KNIFE1 gyroY\", \"Accelerometer MILK accX\", \"Accelerometer MILK accY\", \"Accelerometer MILK accZ\", \"Accelerometer MILK gyroX\", \"Accelerometer MILK gyroY\", \"Accelerometer SPOON accX\", \"Accelerometer SPOON accY\", \"Accelerometer SPOON accZ\", \"Accelerometer SPOON gyroX\", \"Accelerometer SPOON gyroY\", \"Accelerometer SUGAR accX\", \"Accelerometer SUGAR accY\", \"Accelerometer SUGAR accZ\", \"Accelerometer SUGAR gyroX\", \"Accelerometer SUGAR gyroY\", \"Accelerometer KNIFE2 accX\", \"Accelerometer KNIFE2 accY\", \"Accelerometer KNIFE2 accZ\", \"Accelerometer KNIFE2 gyroX\", \"Accelerometer KNIFE2 gyroY\", \"Accelerometer PLATE accX\", \"Accelerometer PLATE accY\", \"Accelerometer PLATE accZ\", \"Accelerometer PLATE gyroX\", \"Accelerometer PLATE gyroY\", \"Accelerometer GLASS accX\", \"Accelerometer GLASS accY\", \"Accelerometer GLASS accZ\", \"Accelerometer GLASS gyroX\", \"Accelerometer GLASS gyroY\", \"REED SWITCH DISHWASHER S1\", \"REED SWITCH FRIDGE S3\", \"REED SWITCH FRIDGE S2\", \"REED SWITCH FRIDGE S1\", \"REED SWITCH MIDDLEDRAWER S1\", \"REED SWITCH MIDDLEDRAWER S2\", \"REED SWITCH MIDDLEDRAWER S3\", \"REED SWITCH LOWERDRAWER S3\", \"REED SWITCH LOWERDRAWER S2\", \"REED SWITCH UPPERDRAWER\", \"REED SWITCH DISHWASHER S3\", \"REED SWITCH LOWERDRAWER S1\", \"REED SWITCH DISHWASHER S2\", \"Accelerometer DOOR1 accX\", \"Accelerometer DOOR1 accY\", \"Accelerometer DOOR1 accZ\", \"Accelerometer LAZYCHAIR accX\", \"Accelerometer LAZYCHAIR accY\", \"Accelerometer LAZYCHAIR accZ\", \"Accelerometer DOOR2 accX\", \"Accelerometer DOOR2 accY\", \"Accelerometer DOOR2 accZ\", \"Accelerometer DISHWASHER accX\", \"Accelerometer DISHWASHER accY\", \"Accelerometer DISHWASHER accZ\", \"Accelerometer UPPERDRAWER accX\", \"Accelerometer UPPERDRAWER accY\", \"Accelerometer UPPERDRAWER accZ\", \"Accelerometer LOWERDRAWER accX\", \"Accelerometer LOWERDRAWER accY\", \"Accelerometer LOWERDRAWER accZ\", \"Accelerometer MIDDLEDRAWER accX\", \"Accelerometer MIDDLEDRAWER accY\", \"Accelerometer MIDDLEDRAWER accZ\", \"Accelerometer FRIDGE accX\", \"Accelerometer FRIDGE accY\", \"Accelerometer FRIDGE accZ\", \"LOCATION TAG1 X\", \"LOCATION TAG1 Y\", \"LOCATION TAG1 Z\", \"LOCATION TAG2 X\", \"LOCATION TAG2 Y\", \"LOCATION TAG2 Z\", \"LOCATION TAG3 X\", \"LOCATION TAG3 Y\", \"LOCATION TAG3 Z\", \"LOCATION TAG4 X\", \"LOCATION TAG4 Y\", \"LOCATION TAG4 Z\",\n",
    "    \"HL_Activity\", \"LL_Left_Arm\", \"LL_Left_Arm_Object\", \"LL_Right_Arm\", \"LL_Right_Arm_Object\", \"ML_Both_Arms\"\n",
    "]\n",
    "use_columns = list(set(columns) - set(ignore_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directories /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY already exist\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S1-ADL5.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S2-ADL3.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S3-ADL2.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S4-ADL2.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S2-ADL4.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S3-ADL4.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S1-Drill.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S4-ADL3.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S3-ADL5.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S2-ADL1.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S2-ADL5.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S3-Drill.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S4-Drill.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S4-ADL4.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S1-ADL4.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S1-ADL3.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S3-ADL1.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S3-ADL3.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S1-ADL2.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S1-ADL1.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S4-ADL5.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S4-ADL1.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S2-Drill.dat'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S2-ADL2.dat')]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_type = \"unsupervised\"\n",
    "train_is_normal = False\n",
    "input_type = \"multivariate\"\n",
    "datetime_index = True\n",
    "dataset_type = \"real\"\n",
    "\n",
    "# create target directory\n",
    "dataset_subfolder = os.path.join(input_type, dataset_collection_name)\n",
    "target_subfolder = os.path.join(target_folder, dataset_subfolder)\n",
    "try:\n",
    "    os.makedirs(target_subfolder)\n",
    "    print(f\"Created directories {target_subfolder}\")\n",
    "except FileExistsError:\n",
    "    print(f\"Directories {target_subfolder} already exist\")\n",
    "    pass\n",
    "\n",
    "dm = Datasets(target_folder)\n",
    "experiments = [f for f in source_folder.glob(\"*.dat\")]\n",
    "experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_experiment_file(path: Path) -> List[pd.DataFrame]:\n",
    "    df = pd.read_csv(path, sep=' ', header=None, names=columns, usecols=use_columns)\n",
    "    df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"], unit=\"ms\")\n",
    "    df[\"is_anomaly\"] = 0\n",
    "    df.loc[df[\"Locomotion\"] == 5, \"is_anomaly\"] = 1\n",
    "    del df[\"Locomotion\"]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S1-ADL5.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S1-ADL5.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S2-ADL3.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S2-ADL3.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S3-ADL2.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S3-ADL2.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S4-ADL2.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S4-ADL2.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S2-ADL4.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S2-ADL4.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S3-ADL4.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S3-ADL4.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S1-Drill.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S1-Drill.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S4-ADL3.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S4-ADL3.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S3-ADL5.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S3-ADL5.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S2-ADL1.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S2-ADL1.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S2-ADL5.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S2-ADL5.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S3-Drill.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S3-Drill.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S4-Drill.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S4-Drill.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S4-ADL4.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S4-ADL4.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S1-ADL4.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S1-ADL4.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S1-ADL3.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S1-ADL3.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S3-ADL1.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S3-ADL1.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S3-ADL3.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S3-ADL3.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S1-ADL2.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S1-ADL2.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S1-ADL1.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S1-ADL1.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S4-ADL5.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S4-ADL5.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S4-ADL1.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S4-ADL1.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S2-Drill.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S2-Drill.test.csv\n",
      "Processed source dataset /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/OPPORTUNITY/dataset/S2-ADL2.dat -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/OPPORTUNITY/S2-ADL2.test.csv\n"
     ]
    }
   ],
   "source": [
    "for exp in experiments:\n",
    "    # transform file to get datasets\n",
    "    df = transform_experiment_file(exp)\n",
    "    # get target filenames\n",
    "    experiment_name = os.path.splitext(exp.name)[0]\n",
    "    dataset_name = f\"{experiment_name}\"\n",
    "    filename = f\"{dataset_name}.test.csv\"\n",
    "    path = os.path.join(dataset_subfolder, filename)\n",
    "    target_filepath = os.path.join(target_subfolder, filename)\n",
    "\n",
    "    # calc length and save in file\n",
    "    dataset_length = len(df)\n",
    "    df.to_csv(target_filepath, index=False)\n",
    "    print(f\"Processed source dataset {exp} -> {target_filepath}\")\n",
    "\n",
    "    # save metadata\n",
    "    dm.add_dataset((dataset_collection_name, dataset_name),\n",
    "        train_path = None,\n",
    "        test_path = path,\n",
    "        dataset_type = dataset_type,\n",
    "        datetime_index = datetime_index,\n",
    "        split_at = None,\n",
    "        train_type = train_type,\n",
    "        train_is_normal = train_is_normal,\n",
    "        input_type = input_type,\n",
    "        dataset_length = dataset_length\n",
    "    )\n",
    "\n",
    "dm.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>train_path</th>\n",
       "      <th>test_path</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>datetime_index</th>\n",
       "      <th>split_at</th>\n",
       "      <th>train_type</th>\n",
       "      <th>train_is_normal</th>\n",
       "      <th>input_type</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection_name</th>\n",
       "      <th>dataset_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"24\" valign=\"top\">OPPORTUNITY</th>\n",
       "      <th>S1-ADL1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S1-ADL1.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>51116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1-ADL2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S1-ADL2.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>32224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1-ADL3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S1-ADL3.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>33273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1-ADL4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S1-ADL4.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>32955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1-ADL5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S1-ADL5.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>30127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S1-Drill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S1-Drill.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>54966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2-ADL1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S2-ADL1.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>42797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2-ADL2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S2-ADL2.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>30182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2-ADL3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S2-ADL3.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>34232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2-ADL4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S2-ADL4.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>32748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2-ADL5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S2-ADL5.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>31826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2-Drill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S2-Drill.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>53398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3-ADL1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S3-ADL1.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>37223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3-ADL2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S3-ADL2.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>27825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3-ADL3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S3-ADL3.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>26717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3-ADL4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S3-ADL4.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>27681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3-ADL5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S3-ADL5.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>26495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3-Drill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S3-Drill.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>70928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4-ADL1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S4-ADL1.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>41588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4-ADL2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S4-ADL2.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>27737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4-ADL3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S4-ADL3.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>25132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4-ADL4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S4-ADL4.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>22230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4-ADL5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S4-ADL5.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>30527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4-Drill</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/OPPORTUNITY/S4-Drill.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>45460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             train_path  \\\n",
       "collection_name dataset_name              \n",
       "OPPORTUNITY     S1-ADL1             NaN   \n",
       "                S1-ADL2             NaN   \n",
       "                S1-ADL3             NaN   \n",
       "                S1-ADL4             NaN   \n",
       "                S1-ADL5             NaN   \n",
       "                S1-Drill            NaN   \n",
       "                S2-ADL1             NaN   \n",
       "                S2-ADL2             NaN   \n",
       "                S2-ADL3             NaN   \n",
       "                S2-ADL4             NaN   \n",
       "                S2-ADL5             NaN   \n",
       "                S2-Drill            NaN   \n",
       "                S3-ADL1             NaN   \n",
       "                S3-ADL2             NaN   \n",
       "                S3-ADL3             NaN   \n",
       "                S3-ADL4             NaN   \n",
       "                S3-ADL5             NaN   \n",
       "                S3-Drill            NaN   \n",
       "                S4-ADL1             NaN   \n",
       "                S4-ADL2             NaN   \n",
       "                S4-ADL3             NaN   \n",
       "                S4-ADL4             NaN   \n",
       "                S4-ADL5             NaN   \n",
       "                S4-Drill            NaN   \n",
       "\n",
       "                                                               test_path  \\\n",
       "collection_name dataset_name                                               \n",
       "OPPORTUNITY     S1-ADL1        multivariate/OPPORTUNITY/S1-ADL1.test.csv   \n",
       "                S1-ADL2        multivariate/OPPORTUNITY/S1-ADL2.test.csv   \n",
       "                S1-ADL3        multivariate/OPPORTUNITY/S1-ADL3.test.csv   \n",
       "                S1-ADL4        multivariate/OPPORTUNITY/S1-ADL4.test.csv   \n",
       "                S1-ADL5        multivariate/OPPORTUNITY/S1-ADL5.test.csv   \n",
       "                S1-Drill      multivariate/OPPORTUNITY/S1-Drill.test.csv   \n",
       "                S2-ADL1        multivariate/OPPORTUNITY/S2-ADL1.test.csv   \n",
       "                S2-ADL2        multivariate/OPPORTUNITY/S2-ADL2.test.csv   \n",
       "                S2-ADL3        multivariate/OPPORTUNITY/S2-ADL3.test.csv   \n",
       "                S2-ADL4        multivariate/OPPORTUNITY/S2-ADL4.test.csv   \n",
       "                S2-ADL5        multivariate/OPPORTUNITY/S2-ADL5.test.csv   \n",
       "                S2-Drill      multivariate/OPPORTUNITY/S2-Drill.test.csv   \n",
       "                S3-ADL1        multivariate/OPPORTUNITY/S3-ADL1.test.csv   \n",
       "                S3-ADL2        multivariate/OPPORTUNITY/S3-ADL2.test.csv   \n",
       "                S3-ADL3        multivariate/OPPORTUNITY/S3-ADL3.test.csv   \n",
       "                S3-ADL4        multivariate/OPPORTUNITY/S3-ADL4.test.csv   \n",
       "                S3-ADL5        multivariate/OPPORTUNITY/S3-ADL5.test.csv   \n",
       "                S3-Drill      multivariate/OPPORTUNITY/S3-Drill.test.csv   \n",
       "                S4-ADL1        multivariate/OPPORTUNITY/S4-ADL1.test.csv   \n",
       "                S4-ADL2        multivariate/OPPORTUNITY/S4-ADL2.test.csv   \n",
       "                S4-ADL3        multivariate/OPPORTUNITY/S4-ADL3.test.csv   \n",
       "                S4-ADL4        multivariate/OPPORTUNITY/S4-ADL4.test.csv   \n",
       "                S4-ADL5        multivariate/OPPORTUNITY/S4-ADL5.test.csv   \n",
       "                S4-Drill      multivariate/OPPORTUNITY/S4-Drill.test.csv   \n",
       "\n",
       "                             dataset_type  datetime_index  split_at  \\\n",
       "collection_name dataset_name                                          \n",
       "OPPORTUNITY     S1-ADL1              real            True       NaN   \n",
       "                S1-ADL2              real            True       NaN   \n",
       "                S1-ADL3              real            True       NaN   \n",
       "                S1-ADL4              real            True       NaN   \n",
       "                S1-ADL5              real            True       NaN   \n",
       "                S1-Drill             real            True       NaN   \n",
       "                S2-ADL1              real            True       NaN   \n",
       "                S2-ADL2              real            True       NaN   \n",
       "                S2-ADL3              real            True       NaN   \n",
       "                S2-ADL4              real            True       NaN   \n",
       "                S2-ADL5              real            True       NaN   \n",
       "                S2-Drill             real            True       NaN   \n",
       "                S3-ADL1              real            True       NaN   \n",
       "                S3-ADL2              real            True       NaN   \n",
       "                S3-ADL3              real            True       NaN   \n",
       "                S3-ADL4              real            True       NaN   \n",
       "                S3-ADL5              real            True       NaN   \n",
       "                S3-Drill             real            True       NaN   \n",
       "                S4-ADL1              real            True       NaN   \n",
       "                S4-ADL2              real            True       NaN   \n",
       "                S4-ADL3              real            True       NaN   \n",
       "                S4-ADL4              real            True       NaN   \n",
       "                S4-ADL5              real            True       NaN   \n",
       "                S4-Drill             real            True       NaN   \n",
       "\n",
       "                                train_type  train_is_normal    input_type  \\\n",
       "collection_name dataset_name                                                \n",
       "OPPORTUNITY     S1-ADL1       unsupervised            False  multivariate   \n",
       "                S1-ADL2       unsupervised            False  multivariate   \n",
       "                S1-ADL3       unsupervised            False  multivariate   \n",
       "                S1-ADL4       unsupervised            False  multivariate   \n",
       "                S1-ADL5       unsupervised            False  multivariate   \n",
       "                S1-Drill      unsupervised            False  multivariate   \n",
       "                S2-ADL1       unsupervised            False  multivariate   \n",
       "                S2-ADL2       unsupervised            False  multivariate   \n",
       "                S2-ADL3       unsupervised            False  multivariate   \n",
       "                S2-ADL4       unsupervised            False  multivariate   \n",
       "                S2-ADL5       unsupervised            False  multivariate   \n",
       "                S2-Drill      unsupervised            False  multivariate   \n",
       "                S3-ADL1       unsupervised            False  multivariate   \n",
       "                S3-ADL2       unsupervised            False  multivariate   \n",
       "                S3-ADL3       unsupervised            False  multivariate   \n",
       "                S3-ADL4       unsupervised            False  multivariate   \n",
       "                S3-ADL5       unsupervised            False  multivariate   \n",
       "                S3-Drill      unsupervised            False  multivariate   \n",
       "                S4-ADL1       unsupervised            False  multivariate   \n",
       "                S4-ADL2       unsupervised            False  multivariate   \n",
       "                S4-ADL3       unsupervised            False  multivariate   \n",
       "                S4-ADL4       unsupervised            False  multivariate   \n",
       "                S4-ADL5       unsupervised            False  multivariate   \n",
       "                S4-Drill      unsupervised            False  multivariate   \n",
       "\n",
       "                              length  \n",
       "collection_name dataset_name          \n",
       "OPPORTUNITY     S1-ADL1        51116  \n",
       "                S1-ADL2        32224  \n",
       "                S1-ADL3        33273  \n",
       "                S1-ADL4        32955  \n",
       "                S1-ADL5        30127  \n",
       "                S1-Drill       54966  \n",
       "                S2-ADL1        42797  \n",
       "                S2-ADL2        30182  \n",
       "                S2-ADL3        34232  \n",
       "                S2-ADL4        32748  \n",
       "                S2-ADL5        31826  \n",
       "                S2-Drill       53398  \n",
       "                S3-ADL1        37223  \n",
       "                S3-ADL2        27825  \n",
       "                S3-ADL3        26717  \n",
       "                S3-ADL4        27681  \n",
       "                S3-ADL5        26495  \n",
       "                S3-Drill       70928  \n",
       "                S4-ADL1        41588  \n",
       "                S4-ADL2        27737  \n",
       "                S4-ADL3        25132  \n",
       "                S4-ADL4        22230  \n",
       "                S4-ADL5        30527  \n",
       "                S4-Drill       45460  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.refresh()\n",
    "dm.df().loc[(slice(dataset_collection_name, dataset_collection_name), slice(None))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimentation\n",
    "\n",
    "Annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Track name</th>\n",
       "      <th>Label name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unique index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Locomotion</td>\n",
       "      <td>Stand</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Locomotion</td>\n",
       "      <td>Walk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Locomotion</td>\n",
       "      <td>Sit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Locomotion</td>\n",
       "      <td>Lie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>HL_Activity</td>\n",
       "      <td>Relaxing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406508</th>\n",
       "      <td>ML_Both_Arms</td>\n",
       "      <td>Open Drawer 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404508</th>\n",
       "      <td>ML_Both_Arms</td>\n",
       "      <td>Close Drawer 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408512</th>\n",
       "      <td>ML_Both_Arms</td>\n",
       "      <td>Clean Table</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407521</th>\n",
       "      <td>ML_Both_Arms</td>\n",
       "      <td>Drink from Cup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405506</th>\n",
       "      <td>ML_Both_Arms</td>\n",
       "      <td>Toggle Switch</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Track name      Label name\n",
       "Unique index                              \n",
       "1               Locomotion           Stand\n",
       "2               Locomotion            Walk\n",
       "4               Locomotion             Sit\n",
       "5               Locomotion             Lie\n",
       "101            HL_Activity        Relaxing\n",
       "...                    ...             ...\n",
       "406508        ML_Both_Arms   Open Drawer 3\n",
       "404508        ML_Both_Arms  Close Drawer 3\n",
       "408512        ML_Both_Arms     Clean Table\n",
       "407521        ML_Both_Arms  Drink from Cup\n",
       "405506        ML_Both_Arms   Toggle Switch\n",
       "\n",
       "[98 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = pd.read_csv(source_folder / \"label_legend.txt\", sep=\"   -   \", index_col=0, engine=\"python\")\n",
    "labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Column names (headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>Accelerometer RKN^ accX</th>\n",
       "      <th>Accelerometer RKN^ accY</th>\n",
       "      <th>Accelerometer RKN^ accZ</th>\n",
       "      <th>Accelerometer HIP accX</th>\n",
       "      <th>Accelerometer HIP accY</th>\n",
       "      <th>Accelerometer HIP accZ</th>\n",
       "      <th>Accelerometer LUA^ accX</th>\n",
       "      <th>Accelerometer LUA^ accY</th>\n",
       "      <th>Accelerometer LUA^ accZ</th>\n",
       "      <th>...</th>\n",
       "      <th>InertialMeasurementUnit R-SHOE AngVelNavFrameY</th>\n",
       "      <th>InertialMeasurementUnit R-SHOE AngVelNavFrameZ</th>\n",
       "      <th>InertialMeasurementUnit R-SHOE Compass</th>\n",
       "      <th>Locomotion</th>\n",
       "      <th>HL_Activity</th>\n",
       "      <th>LL_Left_Arm</th>\n",
       "      <th>LL_Left_Arm_Object</th>\n",
       "      <th>LL_Right_Arm</th>\n",
       "      <th>LL_Right_Arm_Object</th>\n",
       "      <th>ML_Both_Arms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970-01-01 00:00:00.000</td>\n",
       "      <td>87.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>-287.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970-01-01 00:00:00.033</td>\n",
       "      <td>124.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>-389.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>968.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-01-01 00:00:00.067</td>\n",
       "      <td>102.0</td>\n",
       "      <td>996.0</td>\n",
       "      <td>-440.0</td>\n",
       "      <td>-49.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970-01-01 00:00:00.100</td>\n",
       "      <td>59.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>-384.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1970-01-01 00:00:00.133</td>\n",
       "      <td>119.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>-426.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>51111</th>\n",
       "      <td>1970-01-01 00:28:23.683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51112</th>\n",
       "      <td>1970-01-01 00:28:23.716</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51113</th>\n",
       "      <td>1970-01-01 00:28:23.750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51114</th>\n",
       "      <td>1970-01-01 00:28:23.783</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51115</th>\n",
       "      <td>1970-01-01 00:28:23.816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51116 rows × 141 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    timestamp  Accelerometer RKN^ accX  \\\n",
       "0     1970-01-01 00:00:00.000                     87.0   \n",
       "1     1970-01-01 00:00:00.033                    124.0   \n",
       "2     1970-01-01 00:00:00.067                    102.0   \n",
       "3     1970-01-01 00:00:00.100                     59.0   \n",
       "4     1970-01-01 00:00:00.133                    119.0   \n",
       "...                       ...                      ...   \n",
       "51111 1970-01-01 00:28:23.683                      NaN   \n",
       "51112 1970-01-01 00:28:23.716                      NaN   \n",
       "51113 1970-01-01 00:28:23.750                      NaN   \n",
       "51114 1970-01-01 00:28:23.783                      NaN   \n",
       "51115 1970-01-01 00:28:23.816                      NaN   \n",
       "\n",
       "       Accelerometer RKN^ accY  Accelerometer RKN^ accZ  \\\n",
       "0                        975.0                   -287.0   \n",
       "1                        978.0                   -389.0   \n",
       "2                        996.0                   -440.0   \n",
       "3                        861.0                   -384.0   \n",
       "4                        946.0                   -426.0   \n",
       "...                        ...                      ...   \n",
       "51111                      NaN                      NaN   \n",
       "51112                      NaN                      NaN   \n",
       "51113                      NaN                      NaN   \n",
       "51114                      NaN                      NaN   \n",
       "51115                      NaN                      NaN   \n",
       "\n",
       "       Accelerometer HIP accX  Accelerometer HIP accY  Accelerometer HIP accZ  \\\n",
       "0                        11.0                  1001.0                   163.0   \n",
       "1                        -7.0                  1014.0                   199.0   \n",
       "2                       -49.0                  1024.0                   193.0   \n",
       "3                        -9.0                  1023.0                   202.0   \n",
       "4                       -22.0                  1026.0                   188.0   \n",
       "...                       ...                     ...                     ...   \n",
       "51111                     NaN                     NaN                     NaN   \n",
       "51112                     NaN                     NaN                     NaN   \n",
       "51113                     NaN                     NaN                     NaN   \n",
       "51114                     NaN                     NaN                     NaN   \n",
       "51115                     NaN                     NaN                     NaN   \n",
       "\n",
       "       Accelerometer LUA^ accX  Accelerometer LUA^ accY  \\\n",
       "0                         95.0                    975.0   \n",
       "1                        124.0                    968.0   \n",
       "2                        127.0                   1001.0   \n",
       "3                        110.0                   1007.0   \n",
       "4                         98.0                   1001.0   \n",
       "...                        ...                      ...   \n",
       "51111                      NaN                      NaN   \n",
       "51112                      NaN                      NaN   \n",
       "51113                      NaN                      NaN   \n",
       "51114                      NaN                      NaN   \n",
       "51115                      NaN                      NaN   \n",
       "\n",
       "       Accelerometer LUA^ accZ  ...  \\\n",
       "0                        152.0  ...   \n",
       "1                        123.0  ...   \n",
       "2                        113.0  ...   \n",
       "3                        106.0  ...   \n",
       "4                         92.0  ...   \n",
       "...                        ...  ...   \n",
       "51111                      NaN  ...   \n",
       "51112                      NaN  ...   \n",
       "51113                      NaN  ...   \n",
       "51114                      NaN  ...   \n",
       "51115                      NaN  ...   \n",
       "\n",
       "       InertialMeasurementUnit R-SHOE AngVelNavFrameY  \\\n",
       "0                                                20.0   \n",
       "1                                                17.0   \n",
       "2                                               -27.0   \n",
       "3                                               -26.0   \n",
       "4                                               -22.0   \n",
       "...                                               ...   \n",
       "51111                                             NaN   \n",
       "51112                                             NaN   \n",
       "51113                                             NaN   \n",
       "51114                                             NaN   \n",
       "51115                                             NaN   \n",
       "\n",
       "       InertialMeasurementUnit R-SHOE AngVelNavFrameZ  \\\n",
       "0                                                42.0   \n",
       "1                                                31.0   \n",
       "2                                                15.0   \n",
       "3                                                -2.0   \n",
       "4                                                -7.0   \n",
       "...                                               ...   \n",
       "51111                                             NaN   \n",
       "51112                                             NaN   \n",
       "51113                                             NaN   \n",
       "51114                                             NaN   \n",
       "51115                                             NaN   \n",
       "\n",
       "       InertialMeasurementUnit R-SHOE Compass  Locomotion  HL_Activity  \\\n",
       "0                                       175.0           0            0   \n",
       "1                                       175.0           0            0   \n",
       "2                                       175.0           0            0   \n",
       "3                                       175.0           0            0   \n",
       "4                                       175.0           0            0   \n",
       "...                                       ...         ...          ...   \n",
       "51111                                     NaN           0            0   \n",
       "51112                                     NaN           0            0   \n",
       "51113                                     NaN           0            0   \n",
       "51114                                     NaN           0            0   \n",
       "51115                                     NaN           0            0   \n",
       "\n",
       "       LL_Left_Arm  LL_Left_Arm_Object  LL_Right_Arm  LL_Right_Arm_Object  \\\n",
       "0                0                   0             0                    0   \n",
       "1                0                   0             0                    0   \n",
       "2                0                   0             0                    0   \n",
       "3                0                   0             0                    0   \n",
       "4                0                   0             0                    0   \n",
       "...            ...                 ...           ...                  ...   \n",
       "51111            0                   0             0                    0   \n",
       "51112            0                   0             0                    0   \n",
       "51113            0                   0             0                    0   \n",
       "51114            0                   0             0                    0   \n",
       "51115            0                   0             0                    0   \n",
       "\n",
       "       ML_Both_Arms  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  \n",
       "...             ...  \n",
       "51111             0  \n",
       "51112             0  \n",
       "51113             0  \n",
       "51114             0  \n",
       "51115             0  \n",
       "\n",
       "[51116 rows x 141 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(source_folder / \"S1-ADL1.dat\", sep=' ', header=None, names=columns, usecols=use_columns)\n",
    "df[\"timestamp\"] = pd.to_datetime(df[\"timestamp\"], unit=\"ms\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iloc[0:20000].plot(x=\"timestamp\", y=[c for c in use_columns if \"BACK\" in c])\n",
    "df.iloc[0:20000].plot(x=\"timestamp\", y=[\"Locomotion\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Locomotion</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp\n",
       "Locomotion           \n",
       "0               13609\n",
       "1               22380\n",
       "2                6543\n",
       "4                7456\n",
       "5                1128"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Locomotion\", \"timestamp\"]].groupby(\"Locomotion\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HL_Activity</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>5701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>12609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>5132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>10235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp\n",
       "HL_Activity           \n",
       "0                15586\n",
       "101               1853\n",
       "102               5701\n",
       "103              12609\n",
       "104               5132\n",
       "105              10235"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"HL_Activity\", \"timestamp\"]].groupby(\"HL_Activity\").count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>Accelerometer RKN^ accX</th>\n",
       "      <th>Accelerometer RKN^ accY</th>\n",
       "      <th>Accelerometer RKN^ accZ</th>\n",
       "      <th>Accelerometer HIP accX</th>\n",
       "      <th>Accelerometer HIP accY</th>\n",
       "      <th>Accelerometer HIP accZ</th>\n",
       "      <th>Accelerometer LUA^ accX</th>\n",
       "      <th>Accelerometer LUA^ accY</th>\n",
       "      <th>Accelerometer LUA^ accZ</th>\n",
       "      <th>...</th>\n",
       "      <th>InertialMeasurementUnit R-SHOE AngVelNavFrameY</th>\n",
       "      <th>InertialMeasurementUnit R-SHOE AngVelNavFrameZ</th>\n",
       "      <th>InertialMeasurementUnit R-SHOE Compass</th>\n",
       "      <th>HL_Activity</th>\n",
       "      <th>LL_Left_Arm</th>\n",
       "      <th>LL_Left_Arm_Object</th>\n",
       "      <th>LL_Right_Arm</th>\n",
       "      <th>LL_Right_Arm_Object</th>\n",
       "      <th>ML_Both_Arms</th>\n",
       "      <th>is_anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1970-01-01 00:00:00.000</td>\n",
       "      <td>87.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>-287.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1970-01-01 00:00:00.033</td>\n",
       "      <td>124.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>-389.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>968.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>...</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1970-01-01 00:00:00.067</td>\n",
       "      <td>102.0</td>\n",
       "      <td>996.0</td>\n",
       "      <td>-440.0</td>\n",
       "      <td>-49.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1970-01-01 00:00:00.100</td>\n",
       "      <td>59.0</td>\n",
       "      <td>861.0</td>\n",
       "      <td>-384.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1970-01-01 00:00:00.133</td>\n",
       "      <td>119.0</td>\n",
       "      <td>946.0</td>\n",
       "      <td>-426.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51111</th>\n",
       "      <td>1970-01-01 00:28:23.683</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51112</th>\n",
       "      <td>1970-01-01 00:28:23.716</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51113</th>\n",
       "      <td>1970-01-01 00:28:23.750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51114</th>\n",
       "      <td>1970-01-01 00:28:23.783</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51115</th>\n",
       "      <td>1970-01-01 00:28:23.816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51116 rows × 141 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    timestamp  Accelerometer RKN^ accX  \\\n",
       "0     1970-01-01 00:00:00.000                     87.0   \n",
       "1     1970-01-01 00:00:00.033                    124.0   \n",
       "2     1970-01-01 00:00:00.067                    102.0   \n",
       "3     1970-01-01 00:00:00.100                     59.0   \n",
       "4     1970-01-01 00:00:00.133                    119.0   \n",
       "...                       ...                      ...   \n",
       "51111 1970-01-01 00:28:23.683                      NaN   \n",
       "51112 1970-01-01 00:28:23.716                      NaN   \n",
       "51113 1970-01-01 00:28:23.750                      NaN   \n",
       "51114 1970-01-01 00:28:23.783                      NaN   \n",
       "51115 1970-01-01 00:28:23.816                      NaN   \n",
       "\n",
       "       Accelerometer RKN^ accY  Accelerometer RKN^ accZ  \\\n",
       "0                        975.0                   -287.0   \n",
       "1                        978.0                   -389.0   \n",
       "2                        996.0                   -440.0   \n",
       "3                        861.0                   -384.0   \n",
       "4                        946.0                   -426.0   \n",
       "...                        ...                      ...   \n",
       "51111                      NaN                      NaN   \n",
       "51112                      NaN                      NaN   \n",
       "51113                      NaN                      NaN   \n",
       "51114                      NaN                      NaN   \n",
       "51115                      NaN                      NaN   \n",
       "\n",
       "       Accelerometer HIP accX  Accelerometer HIP accY  Accelerometer HIP accZ  \\\n",
       "0                        11.0                  1001.0                   163.0   \n",
       "1                        -7.0                  1014.0                   199.0   \n",
       "2                       -49.0                  1024.0                   193.0   \n",
       "3                        -9.0                  1023.0                   202.0   \n",
       "4                       -22.0                  1026.0                   188.0   \n",
       "...                       ...                     ...                     ...   \n",
       "51111                     NaN                     NaN                     NaN   \n",
       "51112                     NaN                     NaN                     NaN   \n",
       "51113                     NaN                     NaN                     NaN   \n",
       "51114                     NaN                     NaN                     NaN   \n",
       "51115                     NaN                     NaN                     NaN   \n",
       "\n",
       "       Accelerometer LUA^ accX  Accelerometer LUA^ accY  \\\n",
       "0                         95.0                    975.0   \n",
       "1                        124.0                    968.0   \n",
       "2                        127.0                   1001.0   \n",
       "3                        110.0                   1007.0   \n",
       "4                         98.0                   1001.0   \n",
       "...                        ...                      ...   \n",
       "51111                      NaN                      NaN   \n",
       "51112                      NaN                      NaN   \n",
       "51113                      NaN                      NaN   \n",
       "51114                      NaN                      NaN   \n",
       "51115                      NaN                      NaN   \n",
       "\n",
       "       Accelerometer LUA^ accZ  ...  \\\n",
       "0                        152.0  ...   \n",
       "1                        123.0  ...   \n",
       "2                        113.0  ...   \n",
       "3                        106.0  ...   \n",
       "4                         92.0  ...   \n",
       "...                        ...  ...   \n",
       "51111                      NaN  ...   \n",
       "51112                      NaN  ...   \n",
       "51113                      NaN  ...   \n",
       "51114                      NaN  ...   \n",
       "51115                      NaN  ...   \n",
       "\n",
       "       InertialMeasurementUnit R-SHOE AngVelNavFrameY  \\\n",
       "0                                                20.0   \n",
       "1                                                17.0   \n",
       "2                                               -27.0   \n",
       "3                                               -26.0   \n",
       "4                                               -22.0   \n",
       "...                                               ...   \n",
       "51111                                             NaN   \n",
       "51112                                             NaN   \n",
       "51113                                             NaN   \n",
       "51114                                             NaN   \n",
       "51115                                             NaN   \n",
       "\n",
       "       InertialMeasurementUnit R-SHOE AngVelNavFrameZ  \\\n",
       "0                                                42.0   \n",
       "1                                                31.0   \n",
       "2                                                15.0   \n",
       "3                                                -2.0   \n",
       "4                                                -7.0   \n",
       "...                                               ...   \n",
       "51111                                             NaN   \n",
       "51112                                             NaN   \n",
       "51113                                             NaN   \n",
       "51114                                             NaN   \n",
       "51115                                             NaN   \n",
       "\n",
       "       InertialMeasurementUnit R-SHOE Compass  HL_Activity  LL_Left_Arm  \\\n",
       "0                                       175.0            0            0   \n",
       "1                                       175.0            0            0   \n",
       "2                                       175.0            0            0   \n",
       "3                                       175.0            0            0   \n",
       "4                                       175.0            0            0   \n",
       "...                                       ...          ...          ...   \n",
       "51111                                     NaN            0            0   \n",
       "51112                                     NaN            0            0   \n",
       "51113                                     NaN            0            0   \n",
       "51114                                     NaN            0            0   \n",
       "51115                                     NaN            0            0   \n",
       "\n",
       "       LL_Left_Arm_Object  LL_Right_Arm  LL_Right_Arm_Object  ML_Both_Arms  \\\n",
       "0                       0             0                    0             0   \n",
       "1                       0             0                    0             0   \n",
       "2                       0             0                    0             0   \n",
       "3                       0             0                    0             0   \n",
       "4                       0             0                    0             0   \n",
       "...                   ...           ...                  ...           ...   \n",
       "51111                   0             0                    0             0   \n",
       "51112                   0             0                    0             0   \n",
       "51113                   0             0                    0             0   \n",
       "51114                   0             0                    0             0   \n",
       "51115                   0             0                    0             0   \n",
       "\n",
       "       is_anomaly  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  \n",
       "...           ...  \n",
       "51111           0  \n",
       "51112           0  \n",
       "51113           0  \n",
       "51114           0  \n",
       "51115           0  \n",
       "\n",
       "[51116 rows x 141 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"is_anomaly\"] = 0\n",
    "df.loc[df[\"Locomotion\"] == 5, \"is_anomaly\"] = 1\n",
    "del df[\"Locomotion\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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